Kozlitina Julia, Xing Chao, Pertsemlidis Alexander, Schucany William R
Donald W. Reynolds Cardiovascular Clinical Research Center, Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390-8591, USA.
Ann Hum Genet. 2010 Sep 1;74(5):429-38. doi: 10.1111/j.1469-1809.2010.00598.x. Epub 2010 Jul 21.
When estimating the power of genetic association studies, the allele and genotype frequencies are often assumed to be known, and the numbers of individuals with each genotype are set equal to their expectations under Hardy-Weinberg equilibrium. In fact, both allele and genotype frequencies are unknown and thus random. It has previously been suggested that ignoring uncertainty in these parameters could lead to inflated power expectations. To overcome the problem, one can average power estimates over the distributions of unknown frequencies. We investigate the power-averaging method and find that, despite the intuitive appeal, it may not improve accuracy in practice, while significantly increasing computational time. For a fixed allele frequency, we show that the amount of overestimation diminishes rapidly with sample size and is completely negligible for N > 200. For an unknown frequency, the result of averaging depends on the genetic model, and may not always provide a more conservative estimate of power. We explore the effect of uncertainty in the factors that determine statistical power of association studies and propose a more economical approach to the power analysis.
在估计基因关联研究的检验效能时，通常假定等位基因和基因型频率是已知的，并且每种基因型的个体数量设定为其在哈迪-温伯格平衡下的期望值。实际上，等位基因和基因型频率都是未知的，因此是随机的。此前有人提出，忽略这些参数的不确定性可能会导致检验效能期望值的虚高。为克服这个问题，可以在未知频率的分布上对等位基因频率估计值求平均值。我们研究了检验效能平均法，发现尽管该方法直观上有吸引力，但在实际中可能无法提高准确性，同时会显著增加计算时间。对于固定的等位基因频率，我们表明高估的程度会随着样本量迅速减小，对于样本量N>200时完全可以忽略不计。对于未知频率，平均的结果取决于遗传模型，并且可能并不总是能提供更保守的检验效能估计值。我们探讨了决定关联研究统计检验效能的因素中不确定性的影响，并提出了一种更经济的检验效能分析方法。
